How to determine the smallest subspace?

In summary, the smallest subspace containing two given subspaces is the sum of those two subspaces, and the smallest subspace containing a list of vectors is the span of that list. The size of a subspace is determined by the number of dimensions it has. To compare the size of two subspaces, one can show that one is a subset of the other, but it is also possible for neither to be smaller than the other. The smallest subspace containing two given subspaces must have the same dimension as the sum of those two subspaces. The requirement of closure under addition ensures that there are no other subspaces that can contain both given subspaces.
  • #1
maNoFchangE
116
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Two examples are:
  1. Given two subspaces ##U## and ##W## in a vector space ##V##, the smallest subspace in ##V## containing those two subspaces mentioned in the beginning is the sum between them, ##U+W##.
  2. The smallest subspace containing vectors in the list ##(u_1,u_2,...,u_n)## is ##\textrm{span}(u_1,u_2,...,u_n)##.
How can one know how small a subspace is? initially I thought it was determined from the number of elements in the subspace, but there infinite number of elements. Can also someone please give an example by giving two subspaces and show the ways to compare which one is smaller than which?
 
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  • #2
maNoFchangE said:
Two examples are:
  1. Given two subspaces ##U## and ##W## in a vector space ##V##, the smallest subspace in ##V## containing those two subspaces mentioned in the beginning is the sum between them, ##U+W##.
  2. The smallest subspace containing vectors in the list ##(u_1,u_2,...,u_n)## is ##\textrm{span}(u_1,u_2,...,u_n)##.
How can one know how small a subspace is? initially I thought it was determined from the number of elements in the subspace, but there infinite number of elements. Can also someone please give an example by giving two subspaces and show the ways to compare which one is smaller than which?
For 1:
##U+W## is the smallest subspace containing ##U## and ##W## means that if ##Z## is as subspace of ##V## with ##U \subseteq Z, W \subseteq Z##, then ##U+W \subseteq Z##.
Similarly for 2. Any subspace containing ##(u_1,u_2,...,u_n)## will also contain ##\textrm{span}(u_1,u_2,...,u_n)##.

Given two subpaces ##U, W##, you show that ##U## is smaller than ##W## by showing ##U \subset W##.
It is of course possible that for two subspaces, neither is smaller than the other.
Example in ##\mathbb R³##:
##U=\{(x,0,0)|x \in \mathbb R\}##,##W=\{(0,y,0)|y \in \mathbb R\}##. Neither subspace is a subset of the other, so there is no smallest of the two.
##U+W=\{(x,y,0)|x,y \in \mathbb R\}## is the smallest subspace of ##\mathbb R³## containing both ##U## and ##W##.
 
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  • #3
maNoFchangE said:
Two examples are:
  1. Given two subspaces ##U## and ##W## in a vector space ##V##, the smallest subspace in ##V## containing those two subspaces mentioned in the beginning is the sum between them, ##U+W##.
  2. The smallest subspace containing vectors in the list ##(u_1,u_2,...,u_n)## is ##\textrm{span}(u_1,u_2,...,u_n)##.
How can one know how small a subspace is? initially I thought it was determined from the number of elements in the subspace, but there infinite number of elements. Can also someone please give an example by giving two subspaces and show the ways to compare which one is smaller than which?

It is the number of dimensions in the subspace. A line has one, a plane two, a solid three, etc.
 
  • #4
Samy_A said:
Given two subpaces U,WU,WU, W, you show that UUU is smaller than WWW by showing U⊂WU⊂WU \subset W.
Thanks, that really makes sense.
Samy_A said:
For 1:
##U+W## is the smallest subspace containing ##U## and ##W## means that if ##Z## is as subspace of ##V## with ##U \subseteq Z, W \subseteq Z##, then ##U+W \subseteq Z##.
I get that ##U\subseteq U+W## and ##W\subseteq U+W##, therefore both ##U## and ##W## are smaller than ##U+W##. But I have a problem with convincing myself that the smallest subspace which contains ##U## and ##W## together is indeed ##U+W##, in other words, how can we be sure that there are no subspaces in ##U+W## which can contain both ##U## and ##W##. I am thinking that it may be because of the requirement of the closure under addition which must be true for a subset to be called subspace, but it's still hazy in my mind and I cannot sort what I am thinking into an ordered logical reasoning.
Hornbein said:
It is the number of dimensions in the subspace. A line has one, a plane two, a solid three, etc.
Do you mean, the smallest subspace which contain ##U## and ##W## must have the same dimension as a subspace formed by adding the two subspaces, i.e. ##U+W##?
 
  • #5
maNoFchangE said:
I get that ##U\subseteq U+W## and ##W\subseteq U+W##, therefore both ##U## and ##W## are smaller than ##U+W##. But I have a problem with convincing myself that the smallest subspace which contains ##U## and ##W## together is indeed ##U+W##, in other words, how can we be sure that there are no subspaces in ##U+W## which can contain both ##U## and ##W##. I am thinking that it may be because of the requirement of the closure under addition which must be true for a subset to be called subspace, but it's still hazy in my mind and I cannot sort what I am thinking into an ordered logical reasoning.
(bolding mine)
What I bolded in your post is indeed the key.
A subspace that contains both ##U## and ##W## must contain all the sums of elements of ##U## and ##W## (as it must be closed under addition). In other words, it must contain ##U+W##. As ##U+W## is a subspace, we conclude it is the smallest subspace containing ##U## and ##W##.
 
  • #6
Alright thanks, at least now I am convinced that I have been going in the right direction in this matter.
 

1. How is the smallest subspace defined?

The smallest subspace is the subspace that contains the fewest number of vectors while still being able to generate the entire vector space through linear combinations. In other words, it is the most efficient set of vectors that can span the entire vector space.

2. What is the process for determining the smallest subspace?

The process for determining the smallest subspace involves finding a set of linearly independent vectors that can span the entire vector space. This can be done through techniques such as Gaussian elimination or using the Gram-Schmidt process to orthogonalize a set of vectors.

3. How do I know when I have found the smallest subspace?

You can determine if you have found the smallest subspace by checking if the set of vectors you have chosen are linearly independent and span the entire vector space. If there is a smaller set of vectors that can achieve this, then you have not yet found the smallest subspace.

4. Can the smallest subspace be different for different vector spaces?

Yes, the smallest subspace can vary for different vector spaces. This is because the number of linearly independent vectors needed to span a vector space can differ depending on the dimension and structure of the vector space. Therefore, the smallest subspace may also differ.

5. How does determining the smallest subspace relate to finding a basis for a vector space?

Finding the smallest subspace is essentially finding a basis for a vector space. A basis is a set of linearly independent vectors that can span the entire vector space, which is exactly what the smallest subspace is. So, determining the smallest subspace is equivalent to finding a basis for a vector space.

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